Method for determining the distance between a transmitter and a receiver

ABSTRACT

The present invention relates to a multiple carrier smoothing method for navigation satellite signals, in particular a three carrier smoothing method for Galileo signals. It provides a smoothed code solution, which is ionosphere-free to the first order and whose noise is reduced to sub-decimeter level. The method involves integer ambiguities, which can be resolved reliably. The sensitivity of the new method to receiver biases and ionospheric delays of the second order is small. The performance of the three carrier smoothing method allows to reduce the averaging interval to ⅕-th of its current standard value. The results refer to pseudo ranges and are geometry independent.

The present invention relates to a method for determining the positionof a receiver using signals transmitted, e.g. by a constellation ofsatellites. The receiver is typically located at low altitudes. Inparticular, the receiver can be located in an aircraft for navigatingduring final approach and even for landing the aircraft, in a ship or avehicle moving over ground. More particularly, the present inventionrelates to a reduced-noise ionosphere-free smoothing of ranging codes.

Satellite navigation is part of nowadays daily life and is used for anincreased number of applications. The signals transmitted by thesatellites and received by receivers comprise at least one ranging codemodulated on at least one carrier. The ranging code is a sequence ofbinary values, called chips. The rate of the ranging code is of theorder of a few Mchips/s, and the frequency of the carrier signal is ofthe order of GHz. Under ideal conditions, the ranging code is suitablefor determining the code phase with an accuracy of the order of 1/1000of a chip. The ranging code is furthermore modulated by information thatallows determining the time of transmission. The combination of the twopieces of information is the basis for the estimation of the distancebetween the transmitter and the receiver.

Real signals are delayed by an unknown delay in the ionosphere, and arecorrupted by multipath propagation to name but a few. The code phase,i.e., the delay determined by the ranging code is rather affected bymultipath and is also rather noisy. Both are much less the case for thecarrier phase. The carrier phase is the phase of the periodic carrieroscillation. It repeats after one wavelength, which often is of an orderof a few centimetres. “Smoothing” using a Hatch filter is a known methodwhich takes advantage of the less affected ambiguous carrier signal forimproving the quality of the code phase estimation. The traditionalHatch filter subtracts the carrier phase from the code phase, filtersthe so obtained signal, and then restores the carrier contribution.

In this connections carrier smoothing is one of the known methods forreducing noise variance. Carrier smoothing is used today for safetycritical applications, like navigation of aircraft during flight andearly final approach.

The problem in satellite navigation is that due to disturbance in theionosphere unexpected and unforeseeable stochastic delays occur. Inorder to eliminate ionosphere induced errors, the use of codecombinations and carrier combinations n the smoothing are desirable. Allcode combinations considered so far have, however, increased the noisevariance. Due to this increased noise variance, the known carriersmoothing concepts cannot be used for high security applications likenavigation of an aircraft during late final approach and landing phase.

Accordingly, what is needed is a reduced-noise ionosphere-free carriersmoothing with increased performances.

The present invention provides a method for determining a more reliableposition of a receiver, when the signals are potentially affected by theionosphere, multipath, and other phenomena that draw an advantage out ofsmoothing, wherein the receiver uses signals on at least three differentfrequencies.

The present invention provides a method for determining the distancebetween a transmitter located above the ionosphere and a receiverlocated below the ionosphere, wherein the receiver receives from thetransmitter a signal comprising a carrier signal having a carrierfrequency and modulated by at least three coded signals having threedifferent frequencies with the coded signals including informationrepresenting the distance between the transmitter and the receiver witha certain inaccuracy, wherein the method comprises

-   -   creating a linear combination of the at least three coded        signals at a predetermined time so as to obtain a combined        pseudo range signal including a relatively inaccurate        information of the distance at said predetermined time wherein        the combined pseudo range signal is substantially free of        ionosphere induced errors,    -   for each modulated frequency signal, determining the phase        shift,    -   creating a first linear combination of the at least three phase        shifts at said predetermined time so as to obtain a first        combined phase shift signal comprising an information of the        phase shift and, accordingly, an accurate information of the        distance with however ambiguities expressed as a rational        multiplicity of the three wavelengths,    -   forming a differential combined signal which results from the        difference between the combined pseudo range signal and the        first combined phase shift signal,    -   low pass filtering the differential combined signal for reducing        noise so as to obtain a filtered differential combined signal,    -   creating a second linear combination of the at least three phase        shifts at said predetermined time so as to obtain a second        combined phase shift signal comprising an information of the        phase shift at said predetermined time and, accordingly, an        accurate information of the distance with however ambiguities        expressed as a rational multiplicity of the three wavelengths,    -   wherein the second combined phase shift signal includes a        distance information which is identical to the distance        information of the first combined phase shift signal,    -   adding the second combined phase shift signal to the filtered        differential combined signal so as to obtain a summing signal,    -   wherein parameters describing the linear combination of the at        least three coded signals and the first and second combinations        of the at least three phase shifts are chosen such that (i) a        combination of the ambiguities of the first and second combined        phase shift signals result in a single ambiguity expressed as an        integer multiplicity of an analytic combined wavelength, (ii)        the noise variance is reduced, and (iii) the ratio of the single        ambiguity and the noise variance is increased,    -   resolving the single ambiguity through an ambiguity estimator to        which the summing signal is fed, and    -   determining the distance based on the output signal of the        ambiguity estimator.

In a certain embodiment of the present invention there is provided amethod as mentioned before wherein resolving the single ambiguity isonly performed after the beginning of the receipt of a signal from thetransmitted and the receiver tracks the received signal so that after acertain period of time no ambiguity resolution is necessary any longer.

According to another aspect of the present invention there is provided amethod as defined before wherein the ambiguity resolution comprises aleast square resolution and a rounding.

The method according to the invention can be used for satellitenavigation and for satellite navigation during late landing phase of anaircraft, navigating of ships, automated control of objects moving overground.

The present invention is based on the idea to use linear combinations ofat least three carrier signals before and after the filtering of thecode signal. Better than state of the art results are obtained, when thecarrier combinations before and after the filter are the same, and boththe code and carrier combinations are geometry preserving, ionospherefree, and optimized to achieve a minimum variance. This can be furtherimproved by allowing the combination before and after the filter to bedifferent. However, this introduces an integer ambiguity between thephase signal combination used before and after the filter, which must beestimated in addition to the position, and the offset of the localclock. There is some freedom in the choice of the linear combination,which is used to maximize the wavelength associated with the differenceof the linear combination before and after the filtering. This greatlysimplifies the estimation of the ambiguity. One possible implementationis to setup a linear equation describing for the linear combinationconsidered and another ionosphere free smoothed solution, which uses thesame carrier combinations before and after the filter. This system ofequations can be solved by the least square method. The non-integerapproximations of the ambiguities are then rounded to the nearestinteger. The resulting probability of wrong detection is rather low, andthe variance of the position also.

By introducing a further linear combination of the at least three phaseshifts a system of equations is created which is over-determined so thatthe chose of the parameters used for the linear combinations forobtaining the pseudo range signal and the first and second combinedphase shift signals can be optimized. One of the conditions for thefirst and second combined phase shift signals is that they includeexactly the same distance information at the same point of time. Anothercondition is that the combination of the ambiguities of the frequenciesresults in a single ambiguity expressed in an integer multiplicity of ananalytic combination wavelength. Accordingly, the signal resulting fromthe addition of the second combined phase shift signal to the filteredsignal still includes ambiguities. However, when choosing the parametersof the linear combinations such that the combination wavelength israther high and, in particular, the ratio of the combination wavelengthand the noise variance is rather high, resolving the ambiguity isfacilitated.

In a further rather broad sense the present invention provides a methodfor estimating the position of a receiver using radio signals,consisting of at least one ranging code modulated on at least threecarriers, wherein the method comprises

-   -   estimating the delay of each code,    -   estimating the phase of each carrier,    -   combining the code delay estimates,    -   smoothing the resulting combination using a combination of at        least three carrier phases, which means subtracting a phase        combination from the code combination, filtering, and finally        adding a phase combination.

In another aspect, the invention relate to a method for estimating theposition of a receiver obtained by smoothing a linear combination of atleast three code based delay measurements by at least three carrierphase measurements comprising

-   -   an ionosphere-free geometry-free linear combination of at least        three code based delay measurements and at least three carrier        based measurements,    -   a filtering of the above linear combination,    -   an ionosphere-free geometry-preserving linear combination of at        least three carrier phase measurements,    -   an estimation of the ambiguity, i.e. an estimation of the        integer number of multiples of the wavelengths inherent in the        smoothed results, and    -   a determination of the position using the above smoothed linear        combination of delay measurements.

In this embodiment the following has to be taken into consideration (ifnecessary):

-   -   an ionosphere-free, geometry-free linear combination of at least        three code based delay measurements and at least three carrier        based measurements—in the present case ionosphere-free means        γ(a+α₁, b+β₁, c+γ₁), and geometry-free means a+b+c−α₁−β₁−γ₁,    -   an ionosphere-free, geometry-preserving linear combination of at        least three carrier phase measurements, in the present case        ionosphere-free means γ(α₂, β₂, γ₂), and geometry preserving        means α₂+β₂+γ₂=1.

Further embodiments of the present invention are shown in the attachedclaims.

The invention will be described in more detail hereinbelow referring tothe drawing which shows schematically the generalized carrier smoothingsetup according to the invention, wherein different carrier combinationsare used in the smoothing process and the recovery of the pseudorange.

1. Introduction

The smoothing of ranging codes using carrier signals is a popularapproach to reduce code noise and code multipath. It was introduced byHatch and is used in the Wide Area Augmentation System (WAAS), as wellas in the European Geostationary Navigation Overlay Service (EGNOS). Itis also considered for Ground Base Augmentation Systems (GBAS).Traditional code smoothing has the inconvenience that the ionosphericdelay affects the code and carrier phase with opposite signs. Longsmoothing intervals and large ionospheric delays are correspondinglyboth critical factors when applying this method. Two alternativeapproaches address this issue. They are based on dual-frequencymeasurements and were introduced by Hwang et al. and Mc. Graw et al.They are called ionospheric-free and divergence-free smoothing,respectively. The availability of an aeronautical CAT III type ofnavigation service using these smoothing approaches was analyzed byKonno et al. Their analysis shows that the noise enhancement of theionosphere-free smoothing is so significant that it does not meetcurrent specifications. Divergence-free smoothing often performs betterbut also implies that service requirements are relaxed in order toachieve an acceptable availability. In the present paper, an alternativethree-frequency approach for Galileo signals is introduced and analyzed.It promises to meet all requirements considered so far. The next sectionsummarizes and comments the state of the art, Section 3 introduces andanalyzes the new algorithm, Section 4 describes the resolution of theinteger ambiguity inherent in the new algorithm, Section 5 discusses thehandling of the bias contribution associated with the user equipment,Section 6 extends the analysis to satellite dependent biases, andSection 7, finally, analyzes the impact of second order ionosphericcorrections.

2. Known Carrier Smoothing Algorithms

The presentation in this section builds on the work of Hwang, Mc. Graw,Bader, Young and Hiro. The notations of these authors are usedthroughout the papers. Let Ψ and Φ denote the code and carriermeasurements, respectively, then χ=Ψ−Φ contains noise and multipath andis the quantity that is being smoothed in a variant of a Hatch Filter.The filter considered has the form:

$\begin{matrix}{{{\overset{\_}{\chi}\left( {t + 1} \right)} = {{\left( {1 - \frac{1}{\tau}} \right){\overset{\_}{\chi}(t)}} + {\frac{1}{\tau}{\chi \left( {t + 1} \right)}}}},} & (1)\end{matrix}$

with τ being the smoothing time constant in terms of number of samples.Typically, samples are collected during 100 [s] at a rate of one sampleper second. The solution of Equation (1) can be derived easily both forfinite and infinite sets of measurements. The solution for the infiniteset of measurements is given by:

$\begin{matrix}{{\overset{\_}{\chi}(t)} = {\frac{1}{\tau}{\sum\limits_{n = 0}^{\infty}{\left( {1 - \frac{1}{\tau}} \right)^{n}{{\chi \left( {t - n} \right)}.}}}}} & (2)\end{matrix}$

The slightly more complex solution for a set of measurements of size Nconverges rather quickly to the above solution for N→∞. If χ is assumedto be stationary additive white gaussian, the noise variance for N→∞becomes:

${E\left\lbrack {{\overset{\_}{\chi}}^{2}(t)} \right\rbrack} = {\frac{1}{{2\tau} - 1}{{E\left\lbrack {\chi^{2}(t)} \right\rbrack}.}}$

The noise variance for large but finite N is within 2% of this valuewhen the measurement set has reached a size of at least N≧2τ. In thecase of stationary white noise, the ideal averaging would be:

${\overset{\_}{\chi}(t)} = {\frac{1}{t}{\sum\limits_{n = 0}^{t - 1}{{\chi \left( {t - n} \right)}.}}}$

Its noise variance

${E\left\lbrack {{\overset{\_}{\chi}}^{2}(t)} \right\rbrack} = {\frac{1}{t}{E\left\lbrack {\chi^{2}(t)} \right\rbrack}}$

tends to 0 as time goes to infinity. Nevertheless, the former averaginghas been preferred so far, since it is capable to adapt to changingconditions, i.e. situations that are not perfectly stationary.

The filters considered express the smoothed output Ψ_(S) in terms of thecarrier measurement Φ and the low pass filtered combination χ in theform:

$\begin{matrix}\begin{matrix}{{\Psi_{S}\left( {t + 1} \right)} = {{\Phi \left( {t + 1} \right)} + {\overset{\_}{\chi}\left( {t + 1} \right)}}} \\{= {{\frac{1}{\tau}{\Psi \left( {t + 1} \right)}} + {\left( {1 - \frac{1}{\tau}} \right){\left( {{\overset{\_}{\Psi}(t)} + {\Phi \left( {t + 1} \right)} - {\overset{\_}{\Phi}(t)}} \right).}}}}\end{matrix} & (3)\end{matrix}$

Note that in the last term of this equation Φ(t)—the phase filteredaccording to Equation (2)—is to be used and not Φ(t). Next, genericexpressions for

Ψ=r+I _(Ψ)+η  (4)

Φ=r+I _(Φ)+ν+ε  (5)

are introduced, with r denoting the range and all quantities that behavesimilarly, including the tropospheric delay, and biases; with I_(Ψ) andI_(Φ) denoting the ionospheric delay of the code and phase components,respectively; with η denoting the code multipath and noise; and εdenoting the corresponding quantities for the carrier. The quantity ν=λNfinally denotes the offset due to the carrier phase ambiguity N. Thecarrier noise ε is sometimes neglected in the literature. This shallalso be the case in the present section, but not in the subsequent ones.Throughout the paper, all noise components are treated mathematically asif they were Additive White Gaussian Noise (AWGN). These noise modelsmight be gaussian overbounds for the real noise. The choice of thevariance will be such that it includes multipath, as well.

Substituting (4) and (5) into Equation (3) leads to the desiredexpression for the smoothed code:

$\begin{matrix}{{\Psi_{S}\left( {t + 1} \right)} = {{\frac{1}{\tau}\left( {{r\left( {t + 1} \right)} + {I_{\Psi}\left( {t + 1} \right)} + {\eta \left( {t + 1} \right)}} \right)} +}} \\{{\left( {1 - \frac{1}{\tau}} \right)\left( {{\overset{\_}{r}(t)} + {{\overset{\_}{I}}_{\Psi}(t)} + {\overset{\_}{\eta}(t)} + {r\left( {t + 1} \right)} + {I_{\Phi}\left( {t + 1} \right)} +} \right.}} \\\left. {v + {ɛ\left( {t + 1} \right)} - {\overset{\_}{r}(t)} - {{\overset{\_}{I}}_{\Phi}(t)} - v - {\overset{\_}{ɛ}(t)}} \right) \\{= {{r\left( {t + 1} \right)} + {\overset{\_}{\eta}\left( {t + 1} \right)} + {\left( {1 - \frac{1}{\tau}} \right)\left( {{ɛ\left( {t + 1} \right)} - {\overset{\_}{ɛ}(t)}} \right)} +}} \\{{{{\overset{\_}{I}}_{\Psi}\left( {t + 1} \right)} + {\left( {1 - \frac{1}{\tau}} \right){\left( {{I_{\Phi}\left( {t + 1} \right)} - {{\overset{\_}{I}}_{\Phi}(t)}} \right).}}}}\end{matrix}$

A closer look at these equations, leads us to the following observationsand conclusions:

-   -   The presence of Φ(t) in Equation (3) is crucial. It prevents a        dependency on the range history.    -   The phase noise occurs both smoothed ε and unsmoothed ε. In a        classical setting, it is much smaller than the code noise (2 mm        versus 65 cm) and is therefore neglected.    -   The ambiguity does not depend on time as long as no cycle slip        occurs. Note that Φ can take any value compatible with the        geometry and the offset inherent in the ambiguity.    -   Any dependence of Ψ on the phase of the signals would lead to an        unresolved ambiguity.    -   Any dependence of Φ on the code would produce unsmoothed noise        contributions.

Three different types of carrier smoothing have been considered so far,which can all be derived from the above equations:

-   1) Traditional single frequency carrier smoothing: In this case Ψ=ρ,    Φ=φ, and I_(Ψ)=−I_(Φ)=I:

${{\rho_{S}\left( {t + 1} \right)} = {{r\left( {t + 1} \right)} + {\overset{\_}{\eta}\left( {t + 1} \right)} + {\overset{\_}{I}(t)} - {\left( {1 - \frac{1}{\tau}} \right)\left( {{I\left( {t + 1} \right)} - {\overset{\_}{I}(t)}} \right)}}},$

-   -   i.e. the smoothed pseudorange is fully affected by the        ionosphere and even worse: it is influenced by changes in the        ionosphere.

-   2) Divergence-free dual frequency smoothing: In this case, the    ionospheric phase delay is double compensated, in order to match it    with the ionospheric code delay: Ψ=ρ,

${\Phi = {\varphi_{1} + {\frac{2}{\alpha}\left( {\varphi_{1} - \varphi_{2}} \right)}}},$

α=f₁ ²/f₂ ²−1. With I₁−I₂=αI₁ the ionospheric contributions becomeI_(Ψ)=I_(Φ)=I:

ρ_(S)(t+1)=r(t+1)+ η(t+1)+I(t+1),

-   -   i.e. the smoothed pseudorange is only affected by the        instantaneous ionosphere.

-   3) Ionosphere-free smoothing: In this case

${\Psi = {\rho_{1} + {\frac{1}{\alpha}\left( {\rho_{1} - \rho_{2}} \right)}}},{\Phi = {\varphi_{1} + {\frac{1}{\alpha}\left( {\varphi_{1} - \varphi_{2}} \right)}}},$

and

ρ_(S)(t+1)=r(t+1)+ η(t+1),

-   -   i.e. the smoothed pseudorange is no longer affected by the        ionosphere but the noise is enhanced to the value:

${\overset{\_}{\eta} = {{\left( {1 + \frac{1}{\alpha}} \right){\overset{\_}{\eta}}_{1}} - {\frac{1}{\alpha}{\overset{\_}{\eta}}_{x}}}},$

-   -   with the subscript _(χ) denoting the second carrier. GPS        currently transmits signals on the L1 carrier at f₁=154f₀, and        on the L2 carrier at f₂=120f₀, with f₀=10.23·10⁶ MHz. Modernized        GPS will provide an additional signal at f₅=115f₀. The noise        enhancement factor becomes 2.98, and 2.59 for the combinations        of L1/L2 and L1/L5, respectively. This noise enhancement        overcompensates the advantage of an eliminated ionosphere as        shown by Konno et al. The combination of divergence-free and        ionosphere-free filters provides a certain level of availability        for a Vertical Alert Limit (VAL) of 10 m. The use of L5 should        further improve the situation somewhat, especially due to the        reduced noise of the broadband signal. However, even under these        circumstances, the L1/L5 combination will not meet the        requirements considered by EUROCAE or the requirements proposed        by Schuster et al. for surface movement.

-   4) Three carrier ionosphere-free combination: Galileo provides three    signals in the aeronautical bands. They are the MBOC signal on L1,    and the equivalent of a BPSK(10) on the E5a (115f₀) and E5b (118f₀)    carriers. This allows to consider combinations of three carriers:

Ψ=aρ _(a) +bρ _(b) +cρ ₁  (6)

-   -   with the indices a, b, and 1 denoting the E5a, E5b, and L1        bands, respectively. If the reference for the ionosphere is        chosen to be in the L1 band, and g_(a)=(154/115)²,        g_(b)=(154/118)² and g₁=1 then

Ψ=(a+b+c)r+(ag _(a) +bg _(b) +cg ₁)I+(aη _(a) +bη _(b) +cη₁)=(a+b+c)r+Γ(a,b,c)I+η  (7)

-   -   In this equation, the notations Γ(a,b,c)=ag_(a)+bg_(b)+cg₁ as        well as η=aη_(a)+bη_(b)+cη₁ were introduced implicitly.        Equation (7) implies that the following equations must hold in        order to preserve the geometry and eliminate the ionosphere:

a+b+c=1

ag _(a) +bg _(b) +cg ₁=0.

The additional degree of freedom introduced by the third carrier is usedto minimize the variance η. For later reuse, the coefficients thatminimize the noise of the code combination are denoted by a′, b′, andc′. Similarly, a minimum noise, ionosphere-free carrier combination canbe found. The coefficients that minimize the noise of the carrier phasecombination are denoted by α′, β′, and γ′. The difference between thetwo cases is that all phase variances are the same, while the codevariances are related by σ_(ρ) _(ab) =σ_(ρ) ₁ /4, which corresponds tothe ratio of the Cramer-Rao bounds for bandwidths of 24 MHz (E5) and 4MHz (L1), respectively. Small bandwidths were chosen in the presentcontext in order to minimize the sensitivity to interference. In termsof performance, this represents a worst case. Increased bandwidths wouldimprove the results. The optimal choice of the coefficients a′, b′, c′,α′, β′, γ′ is shown in the upper entry of Table I. A similar combinationof phases involving L1 and L5/E5a, as well as L2 or E6 was considered byHatch.

The standard deviation of the noise of the code combination is given byσ_(Ψ)=2.22σ_(ρ) ₁ , and is thus slightly smaller than in the two carrierL1/L5 case. Additionally, the variance of the Galileo L1 signal is 3times smaller than the one of the GPS L1 signals, in the case of afilter bandwidth of 4 MHz. Therefore, the three code combination is areal improvement but not a breakthrough. The smoothed ionosphere-freecode solution will be useful for the carrier phase ambiguity resolutionneeded later in the paper.

In the next section, we shall see that a different manner of performingsmoothing leads to much more significant improvements. None of the knownsmoothing algorithms achieve both such low noise, while eliminating the1st order ionospheric delay.

3. Multicombination Smoothing

The setting of the generalized carrier smoothing is shown in FIG. 1. Ituses two different carrier combinations before and after the filter. Thecarrier combination before the filter is used to eliminate the geometryand together with the code combination also the ionosphere in thesmoothing process. The carrier combination after the smoothing filterrestores the geometry and keeps the result ionosphere-free. The use ofdifferent carrier combinations leads to an integer ambiguity that needsto be resolved. This can be done, however, as shall be discussed in thenext section. The new setting can alternatively be interpreted as a codeand carrier combination Ψ−Φ₁+Φ₂ smoothed by the carrier combination Φ₂.

TABLE I COEFFICIENTS FOR AN IONOSPHERE- FREE CODE COMBINATION SMOOTHEDBY A TRADITIONAL HATCH FILTER. a b′ c′ α′ β′ γ′ −2.59 1.5 2.09 −0.84−0.48 2.31 −10.89 10.87 1.03 −0.84 −0.48 2.31 THE UPPER SOLUTIONMINIMIZES THE NOISE. THE LOWER SOLUTION IS NOISIER BUT LEADS TO ASMALLER INFLATION OF BIASES, AS WILL BE DISCUSSED IN LATER SECTIONS.

In the generalized setting, the smoothed code becomes:

$\begin{matrix}{{\Psi_{S}\left( {t + 1} \right)} = {{\Phi_{2}\left( {t + 1} \right)} + {\frac{1}{\tau}\left( {{\Psi \left( {t + 1} \right)} - {\Phi_{1}\left( {t + 1} \right)}} \right)} + {\left( {1 - \frac{1}{\tau}} \right){\left( {{\overset{\_}{\Psi}(t)} - {{\overset{\_}{\Phi}}_{1}(t)}} \right).}}}} & (8)\end{matrix}$

The smoothing algorithms described in the previous section are recoveredin the case Φ₂=Φ₁.

The three combinations Ψ, Φ₁ and Φ₂ described in the figure are formedwith the three codes (see Equation (6)) and carriers of the E5a, E5b andL1 bands:

Φ_(i)=α_(i)φ_(a)+β_(i)φ_(b)+γ_(i)φ₁  (9)

The substitution of the expressions for the carrier signals leads to

Φ_(i)=(α_(i)+β_(i)+γ_(i))r−Γ(α_(i),β_(i),γ_(i))I+(α_(i)ν_(a)+β_(i)∥_(b)+γ_(i)ν₁)+ε_(i)

with the definition ε_(i)=α_(i)ε_(a)+β_(i)ε_(b)+γ_(i)ε₁. Note that theambiguities implicit in ν_(a), ν_(b), and ν₁ do not depend on i—onlytheir weighting depends on the index. The corresponding expressions forthe codes are found in Equation (7). Substituting these two results inEquation (8) leads to

${\Psi_{S}\left( {t + 1} \right)} = {{\left\lbrack {\alpha_{2} + \beta_{2} + \gamma_{2} + {\frac{1}{\tau}\left( {a + b + c - \alpha_{1} - \beta_{1} - \gamma_{1}} \right)}} \right\rbrack r\left( {t + 1} \right)} + {{\left( {1 - \frac{1}{\tau}} \right)\left\lbrack {a + b + c - \alpha_{1} - \beta_{1} - \gamma_{1}} \right\rbrack}{\overset{\_}{r}(t)}} + {\Gamma {\left( {{\alpha_{1} + a},{\beta_{1} + b},{\gamma_{1} + c}} \right)\left\lbrack {{\left( {1 - \frac{1}{\tau}} \right){\overset{\_}{I}(t)}} + {\frac{1}{\tau}{I\left( {t + 1} \right)}}} \right\rbrack}} - {\Gamma \left( {\alpha_{2},\beta_{2},\gamma_{2}} \right){I\left( {t + 1} \right)}} + {\left( {\alpha_{2} - \alpha_{1}} \right)v_{a}} + {\left( {\beta_{2} - \beta_{1}} \right)v_{b}} + {\left( {\gamma_{2} - \gamma_{1}} \right)v_{1}} + {\overset{\_}{\eta}\left( {t + 1} \right)} - {{\overset{\_}{ɛ}}_{1}\left( {t + 1} \right)} + {{ɛ_{2}\left( {t + 1} \right)}.}}$

The first line of the result contains the desired terms. The second lastlines includes the ambiguities, which shall be chosen to be an integermultiple of a combination wavelength. The noise in the last line shallbe minimized. All other lines shall be chosen to vanish. The second linedescribes the dependency on the range history and the third and forthline are the ionospheric terms at the input and output of the filter. Indetail, the conditions read:

α₂+β₂+γ₂=1  (10)

a+b+c−α ₁−β₁−γ₁=0  (11)

Γ(α₂,β₂,γ₂)=0  (12)

Γ(α₁ +a,β ₁ +b,γ ₁ +c)=0  (13)

(α₂−α₁)ν_(a)+(β₂−β₁)ν_(b)+(γ₂−γ₁)ν₁ =ΛN  (14)

These equations lead to a simple equation for the smoothed code:

Ψ_(S)(t+1)=r(t+1)+ΛN+ η(t+1)− ε ₁(t+1)+ε₂(t+1)  (15)

which contains the desired pseudorange r(t+1), a single ambiguity N, asmoothed code noise η(t+1), and a smoothed first carrier combinationnoise ε ₁(t+1), and an unsmoothed second carrier combination noiseε₂(t+1).

Note that the same result could also have been obtained with thefollowing generalization of the filter described in Equation (1):

χ(t+1)=(1−F(D)) χ(t)+F(D)χ(t+1),

with F(D) being a convergent power series in the delay operator D:tt−1.The only difference is that ε ₁(t+1), and η(t+1) would need to becomputed using the generalized filter.

Equation (14) can be rewritten in the form:

${{\left( {\alpha_{2} - \alpha_{1}} \right)\frac{\lambda_{a}}{\Lambda}N_{a}} + {\left( {\beta_{2} - \beta_{1}} \right)\frac{\lambda_{b}}{\Lambda}N_{b}} + {\left( {\gamma_{2} - \gamma_{1}} \right)\frac{\lambda_{1}}{\Lambda}N_{1}}} = {N.}$

A sufficient condition for the left hand side being an integer is that:

$\begin{matrix}\begin{matrix}{{\left( {\alpha_{2} - \alpha_{1}} \right)\frac{\lambda_{a}}{\Lambda}} = {i \in Z}} \\{{\left( {\beta_{2} - \beta_{1}} \right)\frac{\lambda_{b}}{\Lambda}} = {j \in Z}} \\{{\left( {\gamma_{2} - \gamma_{1}} \right)\frac{\lambda_{1}}{\Lambda}} = {k \in Z}}\end{matrix} & (16)\end{matrix}$

Dividing each of the equations by the corresponding λ, summing using theEquations (10)-(11), and inverting leads to:

$\begin{matrix}{\frac{\Lambda}{1 - \left( {a + b + c} \right)} = {\frac{1}{\frac{i}{\lambda_{a}} + \frac{j}{\lambda_{b}} + \frac{k}{\lambda_{1}}}.}} & (17)\end{matrix}$

With f_(a)=115f₀, f_(b)=118f₀, and f₁=154f₀ every possible wavelengthbecomes a fraction of

$\lambda_{0} = {\frac{c}{f_{0}} = {\frac{299792458}{10.23 \cdot 10^{6}} = {{29.3052\lbrack m\rbrack}.}}}$

Thus, the wavelength Λ will be a fraction of λ₀(1−(a+b+c)), see Equation(17). Since the ease of ambiguity resolution highly depends on the ratioof Λ divided by the residual uncertainty about Ψ_(S)(t+1), it isdesirable that Λ be as large as possible. Incidentally, this will be thecase for certain choices of the tuple i, j, k. Define q by:

$q = \frac{\frac{1}{\lambda_{11}}}{\frac{i}{\lambda_{a}} + \frac{j}{\lambda_{b}} + \frac{k}{\lambda_{1}}}$

then under the assumption that i, j, k has been selected, Λ can beeliminated from the above conditions:

λ_(a)(α₂−α₁)/q+iλ ₀(a+b+c)=iλ ₀  (18)

λ_(b)(β₂−β₁)/q+jλ ₀(a+bαc)=jλ ₀  (19)

λ₁(γ₂−γ₁)/q+kλ ₀(a+b+c)=kλ ₀  (20)

The Equations (10)-(13) and (18)-(20) form a system of linear equationsAx=b that can be solved. Since there are 9 unknowns and 7 equations, thekernel has at least dimension 2. De facto it has dimension 3 in theexamples considered. The solution is therefore a superposition of asolution of the inhomogeneous equation Ax=b and a linear combination ofsolutions of the homogenous equation Ay=0. If one first disregards thecondition that the wavelength Λ shall not be reduced too significantly,one would optimize the choice of the combination in such a way as tominimize the noise enhancement. Assuming that the noise of the differentcode and carrier samples are independent and white gaussian leads to thefollowing expression

$\begin{matrix}\begin{matrix}{\sigma_{\overset{\_}{\eta}}^{2} = {E\left\lbrack \left( {{\overset{\_}{\eta}\left( {t + 1} \right)} - {{\overset{\_}{ɛ}}_{1}\left( {t + 1} \right)} + {ɛ_{2}\left( {t + 1} \right)}} \right)^{2} \right\rbrack}} \\{= {{\frac{1}{{2\tau} - 1}\left( {{\left( {a^{2} + b^{2}} \right)\sigma_{{c.a}/b}^{2}} + {c^{2}\sigma_{c{.1}}^{2}}} \right)} +}} \\{{{\frac{1}{{2\tau} - 1}\left( {\alpha_{1}^{2} + \beta_{1}^{2} + \gamma_{1}^{2}} \right)\sigma_{\varphi}^{2}} + {\left( {\alpha_{2}^{2} + \beta_{2}^{2} + \gamma_{2}^{2}} \right)\sigma_{\varphi}^{2}} -}} \\{{2\frac{1}{\tau}\left( {{\alpha_{1}\alpha_{2}} + {\beta_{1}\beta_{2}} + {\gamma_{1}\gamma_{2}}} \right)\sigma_{\varphi}^{2}}}\end{matrix} & (21)\end{matrix}$

with σ_(c,a/b), σ_(c,1), and σ_(φ), denoting the code noise in theE5a/E5b and L1 bands, as well as the phase noise, respectively. Thelatter phase noise is assumed to be identical in all bands. The noise isa combination of thermal noise and multipath. The multipath componentsexperienced at an aircraft receiver are significantly attenuated withrespect to line of sight. This applies to reflections from both theground and the aircraft itself. Steingaβ finds minimum attenuations of14.2 dB in a combination of measurement campaigns and modeling. In aworst case scenario, where both occur and are not suppressed by anyother mechanisms, this can lead to a phase error of up to 3.6°, whichcorresponds to a length of 2 mm. In the case of code noise, a similarinvestigation can be performed. For simplicity, it shall presently beassumed that the error can be controlled to 3 times the Cramer-Raobound. Other assumptions would lead to slightly different numericalvalues. These assumptions together with Equation (21) define the metricfor the determination of the coefficientsz^(T)=(a,b,c,α₁,β₁,γ₁,α₂,β₂,γ₂):

The coefficients c_(i), which describe the weighting of the solutionsy^((j)) of the system of homogeneous equations Ay^((j))=0 associatedwith the linear equations (10)-(13) and (18)-(20), i.e. Ax=b are thenchosen to minimize

TABLE II TWO SOLUTIONS FOR DIFFERENT VALUES OF τ.${{x + {\sum\limits_{j}{c_{j}y^{(j)}}}}}_{w}^{2} = {\min.}$ bias i jk τ Λ[m] noise {square root over (ζ)}[cm] Λ/(2{square root over (ζ)})[cm] a b c α₁ β₁ γ₁ α₂ β₂ γ₂ 4 −5 1  20 3.15 3.8 41 1.5 −0.89 −0.72 0.038  −51.6  64.5 −14.4 −2.25 1.12  2.13 8 −9 1 100 3.18 2.2 71 1.2−0.30 −0.010 0.008 −101.2 115.3 −14.5 −1.35 0.099 2.25 THE FIRSTSOLUTION WAS DETERMINED WITHOUT SPECIAL CONSIDERATION OF BIASES. IT USESTHE FIRST AUXILIARY SOLUTION OF TABLE I FOR FIXING THE AMBIGUITY. THEINCREASED AVERAGING CONSTANT τ = 100 OF THE SECOND SOLUTION WASPARTIALLY USED TO REDUCE THE BIAS OF THE SOLUTION, SEE LATER SECTIONS.IN THIS CASE, THE AMBITUITY FIXING IS BASED ON THE SECOND SOLUTION OFTABLE I.

Using the definition of the matrix

Y _(ij) =−y _(i) ^((j)),

the condition can be rewritten in the formwhich is a least square problem with the solution:

c=(Y ^(T) WY)⁻¹ Y ^(T) Wx.

This completes the first part of the solution. Using c, one can computez=x−Yc, and using z, the noise term ξ=z^(T)Wz and the wavelength Λ. Thespace of values of (i,j,k) can be searched with the criteria of

-   -   maximizing the wavelength Λ,    -   maximizing the ambiguity discrimination Λ/(2√{square root over        (ξ)}), or    -   minimizing the noise variance ξ.

The first result in Table II was obtained by maximizing thediscrimination, and the second one by maximizing the wavelength. Bothsolutions substantially suppress noise. This is best visualized in thecomponents a,b,c of the solution vector z. In a conventionalionosphere-free smoothing—involving E5b and L1—these coefficients wouldbe b=1.422 and c=2.422. The biases, discussed in later sections, arereduced as well.

Another strength of the proposed algorithms is that an excellentperformance is already obtained for the small values of τ=20.

4. Ambiguity Resolution

The resolution of the ambiguities is performed by relating the smoothedpseudorange measurements through the navigation equations

Ψ_(S) =Hξ+ΛN+ η   (22)

This is a set of k equations, in which H is the geometry matrix,described below; Ψ_(S) is the tupel of linear combination of smoothedmeasurements; η is the tupel of associated noise components; N is thetupel of ambiguities; and ξ is the space-time vector

$\xi = {\begin{pmatrix}\overset{\_}{r} \\{c\; \delta}\end{pmatrix}.}$

In this last equation r is the user's position, and δ his clock offset.The geometry matrix is given by

${H = \begin{pmatrix}\left( {\overset{\_}{e}}^{1} \right)^{T} & 1 \\\vdots & \vdots \\\left( {\overset{\_}{e}}^{k} \right)^{T} & 1\end{pmatrix}},$

with ē′ being the unit vector pointing from the satellite i towards theuser location. The matrix only weakly depends on the current position,and is correspondingly determined from an unsmoothed code solution. Inthe case of extreme accuracy requirements, one might use the solution torecompute H.

Note that all statements below also hold if the wet tropospheric delaywas estimated as well. This would add a variable for the vertical wetdelay to ξ, and Niell mapping components to the geometry matrix H. Dueto the strong correlation between the vertical position, the clockoffset, and the tropospheric wet delay, this option is mainly consideredfor reference stations. The dry component of the tropospheric delay canbe modeled to millimeter accuracy, see Elgered.

Equation (22) does not involve any differences amongst satellites. Thestatistics of the various satellite noise components are thereforeindependent in a mathematical sense. The dimensionality of H is k×m withm being the dimensionality of ξ, and k being the number of satellites.The system of Equation (22) has thus m unknown components from ξ and kunknown ambiguities N. Correspondingly, there are in more variables thanequations.

The ionosphere-free code combination considered in Section 2 providesanother k equations. Including these equations, allows to solve thesystem. Since the code combination from Section 2 is rather noisy, it issmoothed using a traditional Hatch filter. Let ψ and φ denote theassociated code and carrier combinations:

ψ=a′ρ _(a) +b′ρ _(b) +c′ρ ₁

φ=α′φ_(a)+β′φ_(b)+γ′φ₁,

then the condition for the solution, to be geometry preserving,ionosphere-free, and to have the least noise variance leads to thepreviously derived solutions, see Table I. The noise variance of thesesolutions is low enough (21.3 cm in the case τ=20 [s] for the uppersolution, and 16.9 cm in the case τ=100 [s] for the lower solution) toresolve the ambiguity with a very low probability of wrong fixing, asshall be seen below. The combined equations now read:

$\begin{matrix}{{\begin{pmatrix}\Psi_{S} \\\psi_{S}\end{pmatrix} = {{\begin{pmatrix}H & {\Lambda 1}^{T} \\H & 0\end{pmatrix}\begin{pmatrix}\xi \\N\end{pmatrix}} + \begin{pmatrix}\overset{\_}{\eta} \\{\overset{\_}{\eta}}^{\prime}\end{pmatrix}}},} & (23)\end{matrix}$

with Ψ_(S), denoting the k-tupel of desired code and carriercombinations and ψ_(S), η, {right arrow over (η)}, N, as well as 1denoting the corresponding tupels of auxiliary smoothed codecombinations, the noise terms of the combinations, the ambiguity of thedesired combination, and the tupel of ones. The crosscorrelation matrixof the noise Σ has the simple form

${\Sigma = \begin{pmatrix}{\sigma_{\overset{\_}{\eta}}^{2}11} & {\sigma_{\overset{\_}{\eta\eta}}^{2}11} \\{\sigma_{\overset{\_}{\eta\eta}}^{2}11} & {\sigma_{\overset{\_}{\eta}}^{2}11}\end{pmatrix}},$

with 11 being a k×k identity matrix and σ _(η) ² being determined byEquation (21). The remaining elements are given byσ_({right arrow over (η)}) ²=z′^(T)Wz′, and σ _(η{right arrow over (η)})²=z′^(T)Wz=z^(T)Wz′, with z′ being defined throughz′^(T)=(a′,b′,c′,α′,β′,y′,α′,β′,γ′). The duplication of the phasecoefficients in the last three components of the tupel z′ is aconsequence of using the same phase combination before and after thefilter for the auxiliary solution.

Let Equation (23) be rewritten in the simplified form

Ψ=MΞ+η  (24)

then the float solution is obtained by solving the weighted least squareproblem with the weights Σ⁻¹, i.e., {circumflex over(Ξ)}=(M^(T)Σ⁻¹M)⁻¹M^(T)Σ⁻¹Ψ. Since η is zero mean, gaussian distributedwith variance Σ, this implies that ΔΞ={circumflex over (Ξ)}−Ξ is alsozero mean, gaussian distributed with variance (M^(T)Σ⁻¹M)⁻¹. Thedistribution of ΔΞ is thus given by

$\begin{matrix}{{p({\Delta\Xi})} = {\frac{1}{\sqrt{\left( {2\pi} \right)^{k + m}{\det\left( \left( {M^{T}{\sum\limits^{- 1}M}} \right)^{- 1} \right)}}}{{\exp \left( {{- \frac{1}{2}}{{\Delta\Xi}}_{M^{T}\Sigma^{- 1}M}^{2}} \right)}.}}} & (25)\end{matrix}$

The special form of M and Σ allows to compute:

$\begin{matrix}{{M^{T}{\sum\limits^{- 1}M}} = {\frac{1}{{\sigma_{\overset{\_}{\eta}}^{2}\sigma_{\overset{\_}{n}}^{2}} - \sigma_{\overset{\_}{\eta}\overset{\_}{\eta}}^{4}}{\begin{pmatrix}{\begin{pmatrix}{\sigma_{\overset{\_}{\eta}}^{2} +} \\{\sigma_{\overset{\_}{\eta}}^{2} -} \\{2\sigma_{\overset{\_}{\eta\eta}}^{2}}\end{pmatrix}H^{T}H} & {\begin{pmatrix}{\sigma_{\overset{\_}{\eta}}^{2} -} \\\sigma_{\overset{\_}{\eta\eta}}^{2}\end{pmatrix}H^{T}\Lambda} \\{\begin{pmatrix}{\sigma_{\overset{\_}{\eta}}^{2} -} \\\sigma_{\overset{\_}{\eta\eta}}^{2}\end{pmatrix}\Lambda \; H} & {\sigma_{\overset{\_}{\eta}}^{2}\Lambda^{2}11}\end{pmatrix}.}}} & (26)\end{matrix}$

Define Δξ={circumflex over (ξ)}−ξ, and ΔN={circumflex over (N)}−N, i.e.:

${{\Delta\Xi} = \begin{pmatrix}{\Delta\xi} \\{\Delta \; N}\end{pmatrix}},$

and let

$\begin{matrix}{{{r = \frac{\sigma_{\overset{\_}{\eta}}^{2} + \sigma_{\overset{\_}{\eta}}^{2} - {2\sigma_{\overset{\_}{\eta}\overset{\_}{\eta}}^{2}}}{{\sigma_{\overset{\_}{\eta}}^{2}\sigma_{\overset{\_}{\eta}}^{2}} - \sigma_{\overset{\_}{\eta\eta}}^{4}}},{p = {{\frac{\sigma_{\overset{\_}{\eta}}^{2} - \sigma_{\overset{\_}{\eta\eta}}^{2}}{\sigma_{\overset{\_}{\eta}}^{2} + \sigma_{\overset{\_}{\eta}}^{2} - {2\sigma_{\overset{\_}{\eta\eta}}^{2}}}\mspace{14mu} q} = \frac{\sigma_{\overset{\_}{\eta}}^{2}}{\sigma_{\overset{\_}{\eta}}^{2} + \sigma_{\overset{\_}{\eta}}^{2} - {2\sigma_{\overset{\_}{\eta\eta}}^{2}}}}},{then}}\begin{matrix}{{{\Delta\Xi}}_{M^{T}{\sum\limits^{- 1}M}}^{2} = {{{\Delta\Xi}^{T}\left( {M^{T}{\sum\limits^{- 1}M}} \right)}{\Delta\Xi}}} \\{= {{r\left( {{\Delta\xi}^{T},{\Delta \; N^{T}}} \right)}\begin{pmatrix}{H^{T}H} & {{pH}^{T}\Lambda} \\{p\; \Lambda \; H} & {q\; \Lambda^{2}11}\end{pmatrix}\begin{pmatrix}{\Delta\xi} \\{\Delta \; N}\end{pmatrix}}} \\{= {r\left\lbrack {{{{H\; {\Delta\xi}} + {p\; {\Lambda\Delta}\; N}}}^{2} + {\left( {q - p^{2}} \right)\Lambda^{2}{{\Delta \; N}}^{2}}} \right\rbrack}}\end{matrix}} & (27)\end{matrix}$

which implies that −r(q−p²)Λ²∥ΔN∥²/2 is an upper bound for the exponentof Equation (25).

The determinant det(M^(T)Σ⁻¹M) is bounded by diagonalizing the matrix

$J = \begin{pmatrix}{H^{T}H} & {p\; H^{T}\Lambda} \\{p\; \Lambda \; H} & {q\; \Lambda^{2}11}\end{pmatrix}$

through an orthogonal transformation D=U^(T)JU. This is always possiblesince the matrix is symmetric. Any orthogonal matrix U has the samenumber of rows and columns and satisfies U^(T)U=1. This implies that

$\begin{matrix}{\left( {\det \left( J^{- 1} \right)} \right)^{- 1} = {\det (J)}} \\{= {\det \left( {U^{T}{JU}} \right)}} \\{= {\det (D)}} \\{{= {{\prod\limits_{i = 1}^{k + m}\; {\mu_{i}(J)}} \leq \left( {\max\limits_{i}\; {\mu_{i}(J)}} \right)^{k + m}}},}\end{matrix}$

with μ_(i)(J) denoting the i-th eigenvalue of J. The maximal eigenvaluecan be bound using Equation (27):

${\max\limits_{i}\; {\mu_{i}(J)}} = {{\max\limits_{\Xi}\frac{\Xi^{T}J\; \Xi}{{\Xi }^{2}}} \leq {{\max \left\{ {{H}^{2},{q\; \Lambda^{2}}} \right\}} + {2{p}\Lambda {{H}.}}}}$

In the case of a standard geometry matrix with m=4, which shall beconsidered in the remaining part of the paper, one has the trivial bound∥H∥²≦2k This bound only depends on the satellite number but not on theconstellation used. Combining all results leads to the bound

$\begin{matrix}{{p({\Delta\Xi})} \leq {\left( \frac{r\left( {\max \left\{ {{2k},{q\; \Lambda^{2}}} \right\} 2{p}\Lambda \sqrt{2k}} \right)}{2\pi} \right)^{\frac{k + 4}{2}}{{\exp \left( {{- \frac{r}{2}}\begin{pmatrix}{q -} \\p^{2}\end{pmatrix}\Lambda^{2}{{\Delta \; N}}^{2}} \right)}.}}} & (28)\end{matrix}$

This bound holds for all values of Δξ, and does not depend on thegeometry H. The probability of wrong fixing is then easily derived bysumming over all possible values of ΔN:

$\begin{matrix}{{p_{wf}\left( {\Delta \; \xi} \right)} = {\sum\limits_{{\Delta > N},{{{\Delta \; N}} > 0}}{p({\Delta\Xi})}}} & (29)\end{matrix}$

The bound does again not depend on Δξ. Explicit values for aconstellation of k=7 satellites are provided in Table III. Theprobability of wrong fixing turns out to be extremely low for smoothinginterval as small as 20 seconds. The results shown in Table III implythat determining a float solution by least square and subsequentrounding is an extremely reliable method for fixing the ambiguities inthe present context.

The fact that the ambiguity resolution is reduced to a least squaresolution and rounding, should make the certification of the procedureeasier than for other approaches. Also note that the ambiguity needsonly be resolved initially or after a major event, such as a loss ofsynchronization. In an inertial aided three carrier case, this is a veryunlikely event.

Once the ambiguities have been fixed, they can be eliminated and theposition and clock offset can be determined using the Ψ component, i.e.the upper component alone. The noise performance of the fixed solutionis found in Table II. The influence of impairments such as orbit andsatellite clock errors, tropospheric delay, ionospheric delays of secondorder and satellite biases are typically compensated by pseudorangecorrections from augmentation systems. In the next two sections, weshall see, however, that the above smoothed code combination alsoreduces the impact of satellite biases, the ionospheric delay of secondorder, as well as receiver biases by itself. The latter is discussedfirst in the next section.

TABLE III EVALUATION OF THE BOUND ON THE PROBABILITY OF WRONG FIXINGP_(wf) AS DESCRIBED BY THE EQUATIONS (29) AS WELL AS (28) IN THE CASETHAT BIASES ARE NOT CONSIDERED, (34) IN THE PRESENCE OF RECEIVER BIASES,AND (38) IN THE PRESENCE OF BIASES THAT MIGHT BE SATELLITE DEPENENT.p_(wf) i j k r no bias rec. bias sat. bias 4 −5 1 20 8 · 10⁻²⁹ 6 · 10⁻²⁶8 · 10⁻²⁰ 8 −9 1 100 1 · 10⁻⁵⁴ 1 · 10⁻⁴⁴ 2 · 10⁻⁴⁰ ALL CASES ASSUME THATTHERE ARE 7 SATELLITES IN VIEW.

5. Receiver Biases

All results discussed so far are applicable in the absence of biasesbetween the different carrier and code signals. Residual receiver biasesmight impact the estimation of position and time if the measurements areused without taking differences. The evaluation of Melbourne-Wubbenacombinations of measurements by Laurichesse and Mercier has shown thatsome biases are rather constant over long periods of time, even at thelevel of a fraction of a wavelength. They, furthermore, found that thebiases can be separated into a satellite contribution w_(n) ^(k) thatadditionally depends on the receiver class n, and a contribution u_(i)that depends on the receiver only. The latter is the same for allsatellites. The dependence of w_(n) ^(k) on the receiver class is due todifferences in the design of the front-ends and digital filters. Thetotal bias experienced by a receiver i from class n observing signalsfrom satellite k is thus given by

w_(n) ^(k)+u_(i).

In the present section it is assumed that the satellite biases w_(n)^(k) are monitored and provided through an external augmentation system.Residual uncorrected contributions are analysed in the next section. Thepresent section addresses the bias u_(i) induced by the receiver. Theuser receiver index i will now be dropped in order to reduce thecomplexity of the expressions in the next steps.

Denote the biases of the code on carrier E5a, E5b and L1 by u_(a), u_(b)and u₁, respectively, and the corresponding biases of the carriers byν₁, ν_(b), and ν₁ then the bias u experienced by the multicombinationsmoothed signal Ψ_(S) is given by (see Equations (8), (6) and (9)):

u=au _(a) +bu _(b) +cu ₁+(α₂−α₁)ν_(a)+(β₂−β₁)ν_(b)+(γ₂−γ₁)ν₁  (30)

Similarly, the bias of the Hatch smoothed ionosphere-free minimum noisecode combination ψ_(S) is given by

u′=a′u _(a) +b′u _(b) +c′u ₁,

Equation (23) then becomes

$\begin{matrix}{{\begin{pmatrix}\Psi_{S} \\\psi_{S}\end{pmatrix} = {{\begin{pmatrix}G & 1 \\G & 1\end{pmatrix}\begin{pmatrix}\overset{\rightarrow}{r} \\{c\; \delta}\end{pmatrix}} + {\Lambda \begin{pmatrix}N \\0\end{pmatrix}} + \begin{pmatrix}{u\; 1} \\{u^{\prime}1}\end{pmatrix} + \begin{pmatrix}\overset{\_}{\eta} \\{\overset{\_}{\eta}}^{\prime}\end{pmatrix}}},} & (31)\end{matrix}$

with G being the spatial part of the geometry matrix H, i.e., the threeleftmost columns. Equation (31) is invariant with respect to thetransformation

u→u+Δ

u′→u′+Δ

cδ→cδ−Δ,

which means that a bias common to all satellites and a clock offsetcannot be separated. This is also evident from the physical observationthat a receiver cannot distinguish a clock offset from a delay in thetransmission line. As a consequence, u′ can be absorbed in the clock,i.e., it is set to 0. Define w by

$\begin{matrix}\begin{matrix}{w = \frac{u - u^{\prime}}{\Lambda}} \\{{= {\frac{1}{\Lambda}\begin{bmatrix}{{\left( {a - a^{\prime}} \right)u_{a}} + {\left( {b - b^{\prime}} \right)u_{b}} + {\left( {c - c^{\prime}} \right)u_{1}} +} \\{{\left( {\alpha_{2} - \alpha_{1}} \right)v_{a}} + {\left( {\beta_{2} - \beta_{1}} \right)v_{b}} + {\left( {\gamma_{2} - \gamma_{1}} \right)v_{1}}}\end{bmatrix}}},}\end{matrix} & (32)\end{matrix}$

and let Ψ_(S) and ψ_(S) be the biased quantities, then Equation (31)becomes:

$\begin{matrix}{{\begin{pmatrix}\Psi_{S} \\\psi_{S}\end{pmatrix} = {{\begin{pmatrix}H \\H\end{pmatrix}\begin{pmatrix}\overset{\rightarrow}{r} \\{c\; \delta}\end{pmatrix}} + {\Lambda \begin{pmatrix}{N + {w\; 1}} \\0\end{pmatrix}} + \begin{pmatrix}\overset{\_}{\eta} \\{\overset{\_}{\eta}}^{\prime}\end{pmatrix}}},} & (33)\end{matrix}$

Under the assumption that the equipment biases have been initiallycalibrated, e.g., using a differential carrier phase solution at theprevious airport in the case of an aeronautical scenario, they can beremoved from the phase measurements. In this case, w becomes fractional.The float solution to Equation (33) is obtained by a weighted leastsquare estimation of the combined ambiguity and fractional biases N+w1,as described in the previous section for the float solution N. Since theresidual bias is fractional, the float solution can be separated into afixed ambiguity and an estimate of the residual bias. The actual valueof the bias is de-weighted whenever the wavelength Λ of the linearcombination is large. The sharpness of the distribution (25) in thevariable ΔN implies that the estimation of Λw is not very noisy. Theestimate is thus used to update a bias filter. The bias filter updateand the fixed ambiguity are finally used to determine the fixed solutionof the absolute position, as well as the combined clock offset and codebias cδ−u′. In the case of more than 4 satellites the validity of thesolution can be verified with a RAIM-type algorithm.

The potential impact of a receiver bias on the probability of wrongfixing can be addressed as follows. Equation (23) is replaced byEquation (33), and thus Equation (27) by

${{\Delta\Xi}}_{M^{T}\Sigma^{- 1}M}^{2} = {r{\left\lfloor {{\begin{matrix}{{H\; {\Delta\xi}} +} \\{p\; {\Lambda \left( {{\Delta \; N} + {\omega \; 1}} \right)}}\end{matrix}}^{2} + {\left( {q - p^{2}} \right)\Lambda^{2}{{{\Delta \; N} + {\omega \; 1}}}^{2}}} \right\rfloor.{Using}}}$N + ω 1 ≤ γ N with${\gamma^{2} = {{\max\limits_{N \neq 0}\frac{{N + {\omega \; 1}}}{N}} \leq {\left( {1 + {\omega }} \right)^{2} + {\left( {k - 1} \right)\omega^{2}}}}},$

for bounding the determinant, again leads to a geometry independentbound:

$\begin{matrix}{{p({\Delta\Xi})} \leq {\left( \frac{r\begin{pmatrix}{{\max \left\{ {{2k},{q\; \Lambda^{2}\gamma^{2}}} \right\}} +} \\{2{p}{\Lambda\gamma}\sqrt{2k}}\end{pmatrix}}{2\pi} \right)^{\frac{k + 4}{2}}{{\exp \left( {{- \frac{r}{2}}\left( {q - p^{2}} \right)\Lambda^{2}{\begin{matrix}{{\Delta \; N} +} \\{\omega \; 1}\end{matrix}}^{2}} \right)}.}}} & (34)\end{matrix}$

This result shows that the probability of wrong fixing is not sensitiveto small biases of the combined solution. As a consequence, it istypically beneficial to optimize the coefficients of the linearcombination in such a manner that the bias of the desired solution,defined by Equation (30), is minimized. Since the signals in the E5 bandcan be processed in a manner that minimizes the relative biases, onewill aim at combinations that fulfil the conditions b≅−a, andβ₂−β₁≅−(α₂−α₁). It shall be assumed in the remaining part of the paperthat the biases in the E5-Band are identical, i.e. u_(b)=u_(a), andν_(b)=ν_(a). Furthermore, it is meaningful to relate the biases to thestandard deviations of the measurements considered:

u_(x)=χσ_(code) _(x) , and ν_(x)=χσ_(phase),

with σ_(code) _(x) , and σ_(phase) being the noise standard deviationfor the code components, and the phase, respectively, and with χ being acoefficient, which characterizes the biases. Since biases are notreduced by averaging, one can determine them, and also needs todetermine them with an accuracy that is higher than the standarddeviation, i.e. χ<1. The actual values chosen are χ=⅛ in the case τ=20,and χ=¼ in the case τ=100. These values were chosen so that the biasesare roughly ½ of the standard deviation of the solution from Table II.The associated probabilities of wrong fixing are listed in Table III.They were obtained using the bound from Equation (34) as well as∥ΔN+ω1∥²≧ν(1−|ω|)² for ∥ΔN∥²=νε{1,2,3}, i.e. for the dominant terms inthe sum of Equation (29).

6. Satellite Biases

Satellite dependent biases cannot be eliminated by the transformationused in the previous section, since they contribute differently to thepseudoranges and phases of the different satellites. On the other handthese biases can be significantly suppressed using an augmentationsystem, as mentioned already. The purpose of the present section is tostudy the potential impact of residual satellite dependent biases on theprobability of wrong fixing.

In the presence of a general bias U, Equation (24) is extended toinclude that bias:

Ψ=MΞ+U+η,

with η staying zero-mean. In this case, the estimate {circumflex over(Ξ)} of Ξ has a bias given by (M^(T)Σ⁻¹M)⁻¹M^(T)Σ⁻¹U, which is also thebias of ΔΞ. The variances stay unaffected. With these modifications, thedistribution p(ΔΞ) becomes:

${p({\Delta\Xi})} = {\frac{1}{\sqrt{\left( {2\pi} \right)^{k + 4}{\det\left( \left( {M^{T}{\sum\limits^{- 1}M}} \right)^{- 1} \right)}}}{{\exp \left( {{- \frac{1}{2}}{\begin{matrix}{{\Delta\Xi} -} \\\left( {M^{T}{\sum\limits^{- 1}U}} \right)\end{matrix}}_{M^{T}\Sigma^{- 1}M}^{2}} \right)}.}}$

Expanding the exponent and disregarding the term quadratic in U leads tothe following upper bound:

$\begin{matrix}{{- \frac{1}{2}}\left( {{{\Delta\Xi}}_{M^{T}\Sigma^{- 1}M}^{2} - {2{\Delta\Xi}^{T}M^{T}\Sigma^{- 1}U}} \right)} & (35)\end{matrix}$

Since Σ is a non-singular symmetric positive definite matrix Σ^(−1/2)exists, and the second term can be bounded using the Cauchy-Schwartzinequality:

|ΔΞ^(T) M ^(T)Σ^(−1/2)Σ^(−1/2) U| ²≦ΔΞ^(T) M ^(T)Σ⁻¹ MΔΞ·U ^(T)Σ⁻¹IU=∥MΔΞ∥ _(Σ) ⁻¹ ² ∥U∥ _(Σ) ⁻¹ ².

Based on this result the exponent can be bounded by

${{- \frac{1}{2}}{{{\Delta\Xi} - {\left( {M^{T}{\sum\limits^{- 1}M}} \right)^{- 1}M^{T}{\sum\limits^{- 1}U}}}}_{M^{T}{\sum\limits^{- 1}M}}^{2}} \leq {{- \frac{1}{2}}{{M\; {\Delta\Xi}}}_{\sum\limits^{- 1}}{\begin{pmatrix}{{{M\; {\Delta\Xi}}}_{\sum\limits^{- 1}} -} \\{2{U}_{\sum\limits_{- 1}}}\end{pmatrix}.}}$

It is thus sufficient that the bias U fulfils a condition of the form:

∥U∥ _(Σ) ⁻¹ ≦κ∥MΔΞ∥ _(Σ) ⁻¹   (36)

with a value of κ smaller than ½. Equation (27) implies that ∥MΔΞ∥_(Σ)⁻¹ ²≧r(q−p²)Λ²∥ΔN∥². The norm of U can be evaluated explicitly asfollows. Let

$U = \begin{pmatrix}u \\u^{\prime}\end{pmatrix}$

be the decomposition of the bias into the bias of the desired linearcombination u and into the bias of the auxiliary solution u′, then somesimple algebra leads to

$\begin{matrix}{{U}_{\Sigma^{- 1}}^{2} = {U^{T}{\sum\limits^{- 1}U}}} \\{= {\frac{1}{\det \; \Sigma}{U^{T}\begin{pmatrix}{\sigma \frac{2}{\eta^{\prime}}11} & {\sigma \frac{2}{\eta \; \eta^{\prime}}11} \\{\sigma \frac{2}{\eta \; \eta^{\prime}}11} & {\sigma \frac{2}{\eta^{\prime}}11}\end{pmatrix}}U}} \\{= {\frac{1}{\det \; \Sigma}\left( {{\sigma \frac{2}{\eta^{\prime}}{u}^{2}} + {\sigma \frac{2}{\eta^{\prime}}{u^{\prime}}^{2}} - {2\sigma \frac{2}{{\eta\eta}^{\prime}}u^{\prime \; T}u}} \right)}} \\{= {{{rq}{u}^{2}} + {{r\left( {1 - {2p} + q} \right)}{u^{\prime}}^{2}} - {2{r\left( {q - p} \right)}u^{\prime \; T}u}}}\end{matrix}$

with r, p, and q as defined in Section 4. Assuming that the largest biascomponent of u is bounded by b, that the largest bias component of u′ isbounded by b′ and that there are k satellites, the above bound becomes(by definition q>p):

∥U∥ _(Σ) ⁻¹ ² ≦kr(gb ²+(1−2p+q)b′ ²+2(q−p)b′b′)≦k max{b,b′} ²r(1+4(q−p))  (37)

It is often worthwhile evaluating the first bound. The associatedprobability of wrong fixing can be several orders of magnitude smaller.Using the Equations (36) and (37), as well as the lower bound on κ∥MΔΞ∥Σleads to the following inequality in the case ∥ΔN∥>0:

∥U∥ _(Σ) ⁻¹ ² ≦kr(gb ²+(1−2p+q)b′ ²+2(q−p)b′b)≦κ² r(q−p ²)Λ²≦κ² r(q−p²)Λ² ∥ΔN∥ ²≦κ² ∥MΔΞ∥ _(Σ) ⁻¹ ²

which implies the following sufficient condition for κ:

$\kappa^{2} \geq {\frac{k\left( {{qb}^{2} + {\left( {1 - {2p} + q} \right)b^{\prime 2}} + {2\left( {q - p} \right)b^{\prime}b}} \right)}{\left( {q - p^{2}} \right)\Lambda^{2}}.}$

For such values of κ, the following bound applies:

$\begin{matrix}{{p({\Delta\Xi})} \leq {\left( \frac{r\begin{pmatrix}{{\max \left\{ {{2k},{q\; \Lambda^{2}}} \right\}} +} \\{2{p}\Lambda \sqrt{2k}}\end{pmatrix}}{2\pi} \right)^{\frac{k + 4}{2}}{\exp \left( {{- \begin{pmatrix}{1 -} \\{2\kappa}\end{pmatrix}}\frac{r}{2}\left( {q - p^{2}} \right)\Lambda^{2}{{\Delta \; N}}^{2}} \right)}}} & (38)\end{matrix}$

The result of the evaluation of this bound for the solutions consideredis listed in the last column of Table III. The evaluation was performedusing the previous assumptions χ=⅛, and χ=¼ for τ=20 and 100 [s],respectively. The biases of the solutions are listed in Table II. Thebias of the second solution includes an L1-bias of 5 [cm], i.e. the biasof the linear combination is 4 times smaller than its largestconstituent. The results show that even satellite dependent biases canbe controlled rather well. These results were obtained under theassumption that the biases are constant in the E5-band, and that thephase biases can be controlled to millimeter level. The latterassumptions seem reasonable but have not yet been verified to fulldepth.

7. Second Order Ionospheric Corrections

The elimination of the first order ionospheric delay was a main aspectof the present paper. With the high targeted overall accuracy and theoccurrence of large coefficients in the phase combination Φ₁, thecorrections of second order could potentially degrade the performance ina non-negligible manner. The size of these corrections on eachindividual carrier is at most a few centimeters, as reported by Hogueand Jakowski. A model commonly used in the literature for describing thesecond order corrections is due to Bassiri and Hajj, Let J and J_(φ)denote these second order corrections to the code and phasemeasurements, respectively, then:

$\begin{matrix}{J = \frac{s}{f^{3}}} & (39) \\{{{J_{\phi} = {{- \frac{1}{2}}J}},{with}}{{s = {7527c{\int_{path}\ {{l}\; n_{c}{\overset{\rightarrow}{B} \cdot \overset{\rightarrow}{e}}}}}},}} & (40)\end{matrix}$

and c denoting the velocity of light, n_(c) the electron density, B theearth's magnetic field, and the unit vector in the direction of wavepropagation. The latter is typically approximated by the unit vectorpointing from the satellite to the user location, i.e., ray bending isneglected. In this case, s can also be written in the form

s= s·ē,

with s being a quantity that needs to be mapped like the totalelectronic content TEC=∫dln_(e) in the single frequency case. Usingthese corrections in Equation (8), as well as the definitionsh_(a)=(154/115)³, h_(b)=(154/118)³, h₁=1, and J=s/f₁ ³ leads to thefollowing overall correction J_(Ψ):

${{J_{\psi}\left( {t + 1} \right)} = {{\begin{bmatrix}{{\left( {a + \frac{\alpha_{1}}{2}} \right)h_{a}} +} \\{{\left( {b + \frac{\beta_{1}}{2}} \right)h_{b}} +} \\{\left( {c + \frac{\gamma_{1}}{2}} \right)h_{1}}\end{bmatrix}\begin{bmatrix}{{\left( {1 - \frac{1}{\tau}} \right){\overset{\_}{J}(t)}} +} \\{\frac{1}{\tau}{J\left( {t + 1} \right)}}\end{bmatrix}} - {\frac{1}{2}\begin{pmatrix}{{\alpha_{2}h_{a}} +} \\{{\beta_{2}h_{b}} +} \\{\gamma_{2}h_{1}}\end{pmatrix}{J\left( {t + 1} \right)}}}},$

which is further evaluated using the definition

ΔJ(t)=J(t+1)− J (t).

ΔJ is the increment with respect to the filtered ionospheric correction.Slant TEC rates of up to 20 TECU/min, have been observed with typicaltimes scales in the order of minutes. This means that ΔJ(t) can besomewhat larger than J(t+1)−J(t), but stays in the same order ofmagnitude.

By substituting J(t+1)= J(t)+ΔJ(t), the second order ionospheric errorbecomes:

${J\; {\psi \left( {t + 1} \right)}} = {{\begin{bmatrix}{{\begin{pmatrix}{a +} \\\frac{\alpha_{1} - \alpha_{2}}{2}\end{pmatrix}h_{a}} +} \\{{\begin{pmatrix}{b +} \\\frac{\beta_{1} - \beta_{2}}{2}\end{pmatrix}h_{b}} +} \\{\begin{pmatrix}{c +} \\\frac{\gamma_{1} - \gamma_{2}}{2}\end{pmatrix}h_{1}}\end{bmatrix}{\overset{\_}{J}(t)}} + {\begin{bmatrix}{{\begin{Bmatrix}{{- \frac{\alpha_{2}}{2}} +} \\{\frac{1}{\tau}\left( {a + \frac{\alpha_{1}}{2}} \right)}\end{Bmatrix}h_{a}} +} \\{{\begin{Bmatrix}{{- \frac{\beta_{2}}{2}} +} \\{\frac{1}{\tau}\left( {b + \frac{\beta_{1}}{2}} \right)}\end{Bmatrix}h_{b}} +} \\{\begin{Bmatrix}{{- \frac{\gamma_{2}}{2}} +} \\{\frac{1}{\tau}\left( {c + \frac{\gamma_{1}}{2}} \right)}\end{Bmatrix}h_{1}}\end{bmatrix}\Delta \; {J(t)}}}$

and a similar expression for J_(Ψ)(t) with a,b,c replaced by a′,b′,c′,and

-   -   replaced by α′,β′,γ′. Since these expressions are similar to        satellite biases, the analysis of the previous section applies.        The result of the numerical evaluation for the two solutions        considered in this paper is provided in Table IV. The        coefficient of the L1 correction is smaller than 1, and the        gradients are suppressed as well. The table also provides the        probability of wrong fixing for a second order L1 correction of        4 centimeters, which is a value that is above average but not        excessive. The ionosphere was assumed to be static. The simple        bounding technique developed here is not sharp enough to cover        the full range of possible second order ionospheric phenomena.        However, it at least shows that the solution is well behaved for        the most frequent second order error magnitudes.

coefficients of i j k r J (t) J(t) p_(wf) 4 −5 1 20 0.87 0.33 3 · 10⁻¹²8 −9 1 100 0.88 0.38 7 · 10⁻²⁰

8. Conclusions

Three carrier linear combinations provide additional degrees of freedomthat can be used to eliminate the ionosphere, and minimize the noise atthe same time. The consideration of pure code combinations yields someimprovement already. The joint consideration of code and carriercombinations in a Hatch filter leads to substantial additionalimprovements up to sub-decimeter accuracies. At initialization, aftercycle slips, or after loss of synchronization integer ambiguities mustbe resolved. This is comparatively simple, since no differences betweensatellite measurements are formed. Geometry independent bounds for theprobability of wrong fixing were derived. Receiver biases are noteliminated in the scenarios considered, and have correspondingly to beaddressed. Investigations by other authors suggest that the receiverbiases are satellite independent and rather stable over time. This ledto a method for tracking such biases. The impact of small, more general,satellite dependent biases could be limited as well. The second ordercorrections to the ionospheric delay is such a generalized bias. It isfound to be attenuated by the linear combinations considered. The methodpresented resolves the issue of unpredictable ionospheric variability,which is a major issue in the aeronautical context. The method requiresgood engineering of satellites and receivers, as well as a moderatelevel of additional algorithmic complexity. It seems capable ofproviding the necessary accuracy and integrity for CAT III landing, andpotentially also for surface movement. The present paper focussed on thebasic principles of the method and on the analysis of individualimpairments. In a next step, simulations for realistic scenarios need tobe performed, and parameters that are relevant in safety of lifeservices, such as protection levels or the availability of integrityneed to be studied. Furthermore, the modeling and estimation of biasesshould be addressed in more depth.

1. A method for estimating the position of a receiver using radiosignals, consisting of at least one ranging code modulated on at leastthree carriers, wherein the method comprises estimating the delay ofeach code, estimating the phase of each carrier, combining the codedelay estimates, smoothing the resulting combination using a combinationof at least three carrier phases, which means subtracting a phasecombination from the code combination, filtering, and finally adding aphase combination.
 2. The method according to claim 1, using the samecarrier combination before and after the filter.
 3. The method accordingto claim 1, using ionosphere free combinations for the code and carrier,with a low noise variance.
 4. The method according to claim 1, whichminimize the noise variance.
 5. The method according to claim 1, usingdifferent carrier combinations before and after the filter.
 6. Themethod according to claim 1, which uses at least two different smoothedcode combinations in order to estimate the user's position, atime-offset like parameter, and the ambiguity resulting from the use ofdifferent carrier combinations before and after the filter.
 7. Themethod according to claim 1, which uses least squares estimation todetermine a float solution and rounding to determine the integerambiguity from the float solution,
 8. The method according to claim 1,which uses at least two different smoothed code combinations in order toestimate the receiver's position, a time-offset like parameter, receiverbiases between the various code and carrier signals, and the ambiguitiesmentioned,
 9. The method according to claim 1 which uses least squaresestimation to determine a float solution and rounding to determine theinteger ambiguity from the float solution.
 10. A method for satellitenavigation, comprising performing the method according to claim 1 forsatellite navigation.
 11. A method for performing a criticalapplication, comprising performing the method according to claim 1 forcarrying out a critical application.
 12. A method for determining thedistance between a transmitter located above the ionosphere and areceiver located below the ionosphere, wherein the receiver receivesfrom the transmitter a signal comprising a carrier signal having acarrier frequency and modulated by at least three coded signals havingthree different frequencies with the coded signals including informationrepresenting the distance between the transmitter and the receiver witha certain inaccuracy, wherein the method comprises creating a linearcombination of the at least three coded signals at a predetermined timeso as to obtain a combined pseudo range signal including a relativelyinaccurate information of the distance at said predetermined timewherein the combined pseudo range signal is substantially free ofionosphere induced errors, for each modulated frequency signal,determining the phase shift, creating a first linear combination of theat least three phase shifts at said predetermined time so as to obtain afirst combined phase shift signal comprising an information of the phaseshift and, accordingly, an accurate information of the distance withhowever ambiguities expressed as a rational multiplicity of the threewavelengths, forming a differential combined signal which results fromthe difference between the combined pseudo range signal and the firstcombined phase shift signal, low pass filtering the differentialcombined signal for reducing noise so as to obtain a filtereddifferential combined signal, creating a second linear combination ofthe at least three phase shifts at said predetermined time so as toobtain a second combined phase shift signal comprising an information ofthe phase shift at said predetermined time and, accordingly, an accurateinformation of the distance with however ambiguities expressed as arational multiplicity of the three wavelengths, wherein the secondcombined phase shift signal includes a distance information which isidentical to the distance information of the first combined phase shiftsignal, adding the second combined phase shift signal to the filtereddifferential combined signal so as to obtain a summing signal, whereinparameters describing the linear combination of the at least three codedsignals and the first and second combinations of the at least threephase shifts are chosen such that (i) a combination of the ambiguitiesof the first and second combined phase shift signals result in a singleambiguity expressed as an integer multiplicity of an analytic combinedwavelength, (ii) the noise variance is reduced, and (iii) the ratio ofthe single ambiguity and the noise variance is increased, resolving thesingle ambiguity through an ambiguity estimator to which the summingsignal is fed, and determining the distance based on the output signalof the ambiguity estimator.
 13. The method according to claim 12,wherein resolving the single ambiguity is only performed after thebeginning of the receipt of a signal from the transmitted and thereceiver tracks the received signal so that after a certain period oftime no ambiguity resolution is necessary any longer.
 14. The methodaccording to claim 12, wherein the ambiguity resolution comprises aleast square resolution and a rounding.
 15. A method for satellitenavigation, comprising performing the method according to claim 12 forsatellite navigation.
 16. The method according to claim 15, wherein theestimating, combining and smoothing steps are performed in conjunctionwith a late landing phase of an aircraft, navigating of ships, orautomated control of objects moving over ground.
 17. A method forestimating the position of a receiver obtained by smoothing a linearcombination of at least three code based delay measurements by at leastthree carrier phase measurements comprising an ionosphere-freegeometry-free linear combination of at least three code based delaymeasurements and at least three carrier based measurements, a filteringof the above linear combination, an ionosphere-free geometry-preservinglinear combination of at least three carrier phase measurements, anestimation of the ambiguity, i.e. an estimation of the integer number ofmultiples of the wavelengths inherent in the smoothed results, and adetermination of the position using the above smoothed linearcombination of delay measurements.
 18. The method according to claim 17with coefficients of the linear combinations optimized in such a mannerthat the noise is reduced.
 19. The method according to claim 17 whichuses a second ionosphere-free geometry-preserving smoothed linearcombinations of at least two code delay measurements in order toestimate the receiver's position, a time-offset like parameter, receiverbiases between the various code and carrier signals, and the ambiguitiesmentioned.
 20. The method according to claim 17 which uses least squaresestimation to determine a float solution and rounding to determine theinteger ambiguity from the float solution.
 21. A method for satellitenavigation, comprising performing the method according to claim 17 forsatellite navigation.
 22. A method for performing a criticalapplication, comprising performing the method according to claim 17 forcarrying out a critical application.